Enhancing Healthcare with Edge AI for Analysis of Cough Detection

IoT technology for cough detection is promising in healthcare and public health monitoring. It offers a real-time, non-intrusive, and data-driven methodology for identifying and analyzing coughing events, ultimately improving our capacity to monitor and address respiratory health issues in a connected world. Nowadays, subjective evaluations are frequently used in clinical measurement, although they are neither accurate nor trustworthy. Advancements in sensor technology, machine learning, and data analytics are developing a cost-effective, proper cough detection system that is increasingly practicable and beneficial. This paper applies the Keras library imported to the neural network to the acoustic characteristics model. A method for detecting cough with the help of a Mobile App is made with Arduino Nano 33 BLE Sense and Edge Impulse Studio. The proposed system can discriminate between cough sounds and other undesired noises from extensive training data with an accuracy of 98%.